How can I efficiently co-design robot morphology and behavior?
A Talent-infused Policy-gradient Approach to Efficient Co-Design of Morphology and Task Allocation Behavior of Multi-Robot Systems
This paper proposes a more efficient method for co-designing the physical robot (morphology) and its control software (behavior) in multi-robot systems, specifically for tasks like delivering aid in a flood. Instead of designing morphology and behavior separately, or through brute-force nested optimization, they introduce "talent" metrics (like range, speed, payload) that connect the two. This simplifies the co-design problem into optimizing these talent trade-offs and then learning the robot's behavior while respecting these talent constraints. This approach, demonstrated on a multi-UAV flood rescue scenario, shows improved performance and reveals distinct morphology/behavior combinations for single robot vs. multi-robot systems.
For LLM-based multi-agent systems, this talent-based approach could translate to defining key capabilities/traits derived from LLM parameters (e.g., context window size, reasoning ability) that influence agent performance in a given environment. This could offer a more efficient way to configure LLMs within a multi-agent system, optimizing for desired collective behavior by first navigating the talent space, then refining the individual agent policies/prompts under these talent constraints. This promises better control over emergent behavior and a more structured design approach for complex multi-agent interactions.